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− | + | The current model is not able to show the expected dependence of violacein yield on promoter strength. After reevaluating our assumptions, we identified some potential flaws of the model that might cause the unexpected results. | |
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− | + | One of the assumptions from our model is that the rate of production of L-tryptophan is constant and independent of the promoter strength. Jones el al. suggest that the L-tryptophan production rate may be affected by the metabolic burden of the production of the recombinant enzymes (VioA, VioB, etc.). This phenomenon may be caused by the depletion of essential metabolic resource, such as amino acids, mRNA and ATP. Therefore, the L-tryptophan production rate might need to be dependent on enzymes production rates. | |
+ | <br><br> | ||
+ | Another effect that we didn’t consider is the saturation of the enzymes. To improve our model, we could include these effects by employing Michaelis-Menten Kinetics equations in our next step. Nevertheless, we have been cautious about including this in our model, since increasing the number of parameters, without increasing the number of data points usually causes the overfitting of the model. | ||
+ | <br><br> | ||
+ | Finally, since the violacein pathway has not been fully characterized, it is possible that we ignored some reactions in the complete pathway. Moreover, there may be feedback loops that regulate the pathway. We will need to investigate these possible components and incorporate them into our model if they prove to be present in the pathway. | ||
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− | + | Here we present a method to fit a model of violacein production in E.coli to experimental data of violacein yield with different promoters using nonlinear regression. Although it fails to calculate the dependence on promoter strength, our model is able predict the average violacein concentration. We expect that small changes on the model, such as including a L-tryptophan production dependence of the metabolic burden, would allow us to successfully predict the violacein production in response to the variation of promoter strength. Once the predictive model is complete, we will be able to find the strains that lead to optimal violacein yield computationally. | |
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− | <div class = "h1" style="color:white"> | + | <div class = "h1" style="color:white">References</div> |
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− | + | <ol> | |
− | + | <li>Carvalho, D. D., Costa, F. T. M., Duran, N., & Haun, M. (2006). Cytotoxic activity of violacein in human colon cancer cells. <i>Toxicology in Vitro</i>, 20(8), 1514–1521. <br><a href="http://dx.doi.org/10.1016/j.tiv.2006.06.007">http://dx.doi.org/10.1016/j.tiv.2006.06.007</a></li> | |
− | + | <li>Jones, J. A., Vernacchio, V. R., Lachance, D. M., Lebovich, M., Fu, L., Shirke, A. N., … Koffas, M. A. G. (2015). ePathOptimize: A Combinatorial Approach for Transcriptional Balancing of Metabolic Pathways. <i>Scientific Reports</i>, 5, 11301. <br><a href="http://doi.org/10.1038/srep11301">http://doi.org/10.1038/srep11301</a></li> | |
+ | <li>Lee, M. E., Aswani, A., Han, A. S., Tomlin, C. J., & Dueber, J. E. (2013). Expression-level optimization of a multi-enzyme pathway in the absence of a high-throughput assay. <i>Nucleic Acids Research</i>, 41(22), 10668–10678. <br> <a href="http://doi.org/10.1093/nar/gkt809">http://doi.org/10.1093/nar/gkt809</a></li> | ||
+ | </ol> | ||
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Revision as of 03:33, 22 November 2016